In [1]:
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import cv2
import glob
import time
from sklearn.svm import LinearSVC
from sklearn.preprocessing import StandardScaler
from skimage.feature import hog
from sklearn.model_selection import train_test_split
from skimage.feature import hog
%matplotlib inline
In [2]:
def get_hog_features(img, orient, pix_per_cell, cell_per_block, vis=False, feature_vec=True):
if vis == True: # Call with two outputs if vis==True to visualize the HOG
features, hog_image = hog(img, orientations=orient,
pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block),
transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features, hog_image
else: # Otherwise call with one output
features = hog(img, orientations=orient,
pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block),
transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features
# Define a function to compute binned color features
def bin_spatial(img, size=(16, 16)):
return cv2.resize(img, size).ravel()
# Define a function to compute color histogram features
def color_hist(img, nbins=32):
ch1 = np.histogram(img[:,:,0], bins=nbins, range=(0, 256))[0]#We need only the histogram, no bins edges
ch2 = np.histogram(img[:,:,1], bins=nbins, range=(0, 256))[0]
ch3 = np.histogram(img[:,:,2], bins=nbins, range=(0, 256))[0]
hist = np.hstack((ch1, ch2, ch3))
return hist
In [3]:
# Define a function to extract features from a list of images
def img_features(feature_image, spatial_feat, hist_feat, hog_feat, hist_bins, orient,
pix_per_cell, cell_per_block, hog_channel):
file_features = []
if spatial_feat == True:
spatial_features = bin_spatial(feature_image, size=spatial_size)
#print 'spat', spatial_features.shape
file_features.append(spatial_features)
if hist_feat == True:
# Apply color_hist()
hist_features = color_hist(feature_image, nbins=hist_bins)
#print 'hist', hist_features.shape
file_features.append(hist_features)
if hog_feat == True:
# Call get_hog_features() with vis=False, feature_vec=True
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.append(get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
hog_features = np.ravel(hog_features)
else:
feature_image = cv2.cvtColor(feature_image, cv2.COLOR_LUV2RGB)
feature_image = cv2.cvtColor(feature_image, cv2.COLOR_RGB2GRAY)
hog_features = get_hog_features(feature_image[:,:], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
#print 'hog', hog_features.shape
# Append the new feature vector to the features list
file_features.append(hog_features)
return file_features
def extract_features(imgs, color_space='RGB', spatial_size=(32, 32),
hist_bins=32, orient=9,
pix_per_cell=8, cell_per_block=2, hog_channel=0,
spatial_feat=True, hist_feat=True, hog_feat=True):
# Create a list to append feature vectors to
features = []
# Iterate through the list of images
for file_p in imgs:
file_features = []
image = cv2.imread(file_p) # Read in each imageone by one
# apply color conversion if other than 'RGB'
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(image)
file_features = img_features(feature_image, spatial_feat, hist_feat, hog_feat, hist_bins, orient,
pix_per_cell, cell_per_block, hog_channel)
features.append(np.concatenate(file_features))
feature_image=cv2.flip(feature_image,1) # Augment the dataset with flipped images
file_features = img_features(feature_image, spatial_feat, hist_feat, hog_feat, hist_bins, orient,
pix_per_cell, cell_per_block, hog_channel)
features.append(np.concatenate(file_features))
return features # Return list of feature vectors
In [4]:
def show_images_in_table (images, table_size, fig_size = (10, 10), cmap=None, titles=None):
sizex = table_size [0]
sizey = table_size [1]
fig, imtable = plt.subplots (sizey, sizex, figsize = fig_size, squeeze=False)
for j in range (sizey):
for i in range (sizex):
im_idx = i + j*sizex
if (isinstance(cmap, (list, tuple))):
imtable [j][i].imshow (images[im_idx], cmap=cmap[i])
else:
im = images[im_idx]
if len(im.shape) == 3:
imtable [j][i].imshow (im)
else:
imtable [j][i].imshow (im, cmap='gray')
imtable [j][i].axis('off')
if not titles is None:
imtable [j][i].set_title (titles [im_idx], fontsize=32)
plt.show ()
def plt_show_gray (image):
plt.figure ()
plt.imshow (image, cmap='gray')
plt.show ()
def plt_show (image):
plt.figure ()
plt.imshow (image)
plt.show ()
In [5]:
# Read in cars and notcars
images = glob.glob('*vehicles/*/*')
cars = []
notcars = []
for image in images:
if 'non-' in image:
notcars.append(image)
else:
cars.append(image)
print(len(cars))
print(len(notcars))
In [6]:
# Define parameters for feature extraction
color_space = 'LUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 8 # HOG orientations
pix_per_cell = 8 # HOG pixels per cell
cell_per_block = 2 # HOG cells per block
hog_channel = 0 # Can be 0, 1, 2, or "ALL"
spatial_size = (16, 16) # Spatial binning dimensions
hist_bins = 32 # Number of histogram bins
spatial_feat = True # Spatial features on or off
hist_feat = True # Histogram features on or off
hog_feat = True # HOG features on or off
In [7]:
# Reading Test images to visualize HOG features
images_to_visualize=glob.glob('test_img/*')
feature_imgs =[]
feature_examples = []
for img in images_to_visualize:
i = cv2.imread(img)
i = cv2.cvtColor(i,cv2.COLOR_BGR2RGB)
feature_imgs.append(i)
feature_examples.extend(feature_imgs)
for img_sample in feature_imgs:
ip_sample = img_sample
ip_sample = cv2.cvtColor(ip_sample,cv2.COLOR_RGB2GRAY)
features, hog_image = get_hog_features(ip_sample, orient, pix_per_cell, cell_per_block, vis=True)
feature_examples.append (hog_image)
print('visualizing HOG features')
show_images_in_table (feature_examples, (6, 2), fig_size=(20, 6))
In [8]:
car_features = extract_features(cars, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
print ('Car samples: ', len(car_features))
notcar_features = extract_features(notcars, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
print ('Notcar samples: ', len(notcar_features))
X = np.vstack((car_features, notcar_features)).astype(np.float64)
X_scaler = StandardScaler().fit(X) # Fit a per-column scaler
scaled_X = X_scaler.transform(X) # Apply the scaler to X
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features)))) # Define the labels vector
# Split up data into randomized training and test sets
X_train, X_test, y_train, y_test = train_test_split(scaled_X, y, test_size=0.2, random_state=22)
In [9]:
print('Training Using:',orient,'orientations', pix_per_cell,
'pixels per cell and', cell_per_block,'cells per block')
print('Feature vector length:', len(X_train[0]))
svc = LinearSVC(loss='hinge') # Use a linear SVC
t=time.time() # Check the training time for the SVC
svc.fit(X_train, y_train) # Train the classifier
t2 = time.time()
print(round(t2-t, 2), 'Seconds to train SVC...')
print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4)) # Check the score of the SVC
In [10]:
print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4)) # Check the score of the SVC
In [11]:
def slide_window(img, x_start_stop=[None, None], y_start_stop=[None, None],
xy_window=(64, 64), xy_overlap=(0.5, 0.5)):
# If x and/or y start/stop positions not defined, set to image size
if x_start_stop[0] == None:
x_start_stop[0] = 0
if x_start_stop[1] == None:
x_start_stop[1] = img.shape[1]
if y_start_stop[0] == None:
y_start_stop[0] = 0
if y_start_stop[1] == None:
y_start_stop[1] = img.shape[0]
# Compute the span of the region to be searched
xspan = x_start_stop[1] - x_start_stop[0]
yspan = y_start_stop[1] - y_start_stop[0]
# Compute the number of pixels per step in x/y
nx_pix_per_step = np.int(xy_window[0]*(1 - xy_overlap[0]))
ny_pix_per_step = np.int(xy_window[1]*(1 - xy_overlap[1]))
# Compute the number of windows in x/y
nx_buffer = np.int(xy_window[0]*(xy_overlap[0]))
ny_buffer = np.int(xy_window[1]*(xy_overlap[1]))
nx_windows = np.int((xspan-nx_buffer)/nx_pix_per_step)
ny_windows = np.int((yspan-ny_buffer)/ny_pix_per_step)
# Initialize a list to append window positions to
window_list = []
# Loop through finding x and y window positions
# Note: you could vectorize this step, but in practice
# you'll be considering windows one by one with your
# classifier, so looping makes sense
for ys in range(ny_windows):
for xs in range(nx_windows):
# Calculate window position
startx = xs*nx_pix_per_step + x_start_stop[0]
endx = startx + xy_window[0]
starty = ys*ny_pix_per_step + y_start_stop[0]
endy = starty + xy_window[1]
# Append window position to list
window_list.append(((startx, starty), (endx, endy)))
# Return the list of windows
return window_list
# Define a function to draw bounding boxes on an image
def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):
imcopy = np.copy(img) # Make a copy of the image
for bbox in bboxes: # Iterate through the bounding boxes
cv2.rectangle(imcopy, bbox[0], bbox[1], color, thick)
return imcopy
In [12]:
def single_img_features(img, color_space='RGB', spatial_size=(32, 32),
hist_bins=32, orient=9,
pix_per_cell=8, cell_per_block=2, hog_channel=0,
spatial_feat=True, hist_feat=True, hog_feat=True):
#1) Define an empty list to receive features
img_features = []
#2) Apply color conversion if other than 'RGB'
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(img)
#3) Compute spatial features if flag is set
if spatial_feat == True:
spatial_features = bin_spatial(feature_image, size=spatial_size)
#4) Append features to list
img_features.append(spatial_features)
#5) Compute histogram features if flag is set
if hist_feat == True:
hist_features = color_hist(feature_image, nbins=hist_bins)
#6) Append features to list
img_features.append(hist_features)
#7) Compute HOG features if flag is set
if hog_feat == True:
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.extend(get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
else:
hog_features = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
#8) Append features to list
img_features.append(hog_features)
#9) Return concatenated array of features
return np.concatenate(img_features)
In [13]:
# Define a function you will pass an image
# and the list of windows to be searched (output of slide_windows())
def search_windows(img, windows, clf, scaler, color_space='RGB',
spatial_size=(32, 32), hist_bins=32,
hist_range=(0, 256), orient=8,
pix_per_cell=8, cell_per_block=2,
hog_channel=0, spatial_feat=True,
hist_feat=True, hog_feat=True):
#1) Create an empty list to receive positive detection windows
on_windows = []
#2) Iterate over all windows in the list
for window in windows:
#3) Extract the test window from original image
test_img = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))
#4) Extract features for that window using single_img_features()
features = single_img_features(test_img, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
#5) Scale extracted features to be fed to classifier
test_features = scaler.transform(np.array(features).reshape(1, -1))
#6) Predict using your classifier
prediction = clf.predict(test_features)
#7) If positive (prediction == 1) then save the window
if prediction == 1:
on_windows.append(window)
#8) Return windows for positive detections
return on_windows
# A function to show an image
def show_img(img):
if len(img.shape)==3: #Color BGR image
plt.figure()
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
else: # Grayscale image
plt.figure()
plt.imshow(img, cmap='gray')
In [14]:
t=time.time() # Start time
for image_p in glob.glob('test_images/test*.jpg'):
image = cv2.imread(image_p)
draw_image = np.copy(image)
windows = slide_window(image, x_start_stop=[None, None], y_start_stop=[400, 640],
xy_window=(128, 128), xy_overlap=(0.85, 0.85))
hot_windows = []
hot_windows += (search_windows(image, windows, svc, X_scaler, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat))
print(len(hot_windows))
window_img = draw_boxes(draw_image, hot_windows, color=(0, 0, 255), thick=6)
show_img(window_img)
print(round(time.time()-t, 2), 'Seconds to process test images')
In [ ]:
In [ ]:
In [15]:
# defining parameters of sliding windows
image = mpimg.imread('test_images/test1.jpg')
window_img = np.copy(image)
sw_x_limits = [
[400, None],
[400, None],
[400, None],
[400, 1280]
]
sw_y_limits = [
[400, 640],
[400, 640],
[400, 600],
[400, 500]
]
sw_window_size = [
(128, 128),
(112,112),
(96, 96),
(80, 80)
]
sw_overlap = [
(0.75, 0.75),
(0.75, 0.75),
(0.75, 0.75),
(0.75, 0.75)
]
# create sliding windows
windows = slide_window(image, x_start_stop=sw_x_limits[0], y_start_stop=sw_y_limits[0],
xy_window=sw_window_size[0], xy_overlap=sw_overlap[0])
windows2 = slide_window(image, x_start_stop=sw_x_limits[1], y_start_stop=sw_y_limits[1],
xy_window=sw_window_size[1], xy_overlap=sw_overlap[1])
windows3 = slide_window(image, x_start_stop=sw_x_limits[2], y_start_stop=sw_y_limits[2],
xy_window=sw_window_size[2], xy_overlap=sw_overlap[2])
windows4 = slide_window(image, x_start_stop=sw_x_limits[3], y_start_stop=sw_y_limits[3],
xy_window=sw_window_size[3], xy_overlap=sw_overlap[3])
# show sliding windows
sliding_windows = []
sliding_windows.append (draw_boxes(np.copy(image), windows, color=(0, 0, 0), thick=4))
sliding_windows.append (draw_boxes(np.copy(image), windows2, color=(0, 0, 0), thick=4))
sliding_windows.append (draw_boxes(np.copy(image), windows3, color=(0, 0, 0), thick=4))
sliding_windows.append (draw_boxes(np.copy(image), windows4, color=(0, 0, 0), thick=4))
# visualizing sliding windows
sliding_windows [0] = draw_boxes (sliding_windows [0], [windows[9]], color=(0, 0, 255), thick=8)
sliding_windows [1] = draw_boxes (sliding_windows [1], [windows2[12]], color=(0, 0, 255), thick=8)
sliding_windows [2] = draw_boxes (sliding_windows [2], [windows3[5]], color=(0, 0, 255), thick=8)
sliding_windows [3] = draw_boxes (sliding_windows [3], [windows3[15]], color=(0, 0, 255), thick=8)
sw_titles = [
'128 x 128',
'112 x 112',
'96 x 96',
'80 x 80'
]
show_images_in_table (sliding_windows, (1, 4), fig_size=(40, 28), titles=sw_titles)
In [16]:
def get_hot_boxes (image):
dst = np.copy (image)
all_hot_windows = []
# iterate over previousely defined sliding windows
for x_limits, y_limits, window_size, overlap in zip (sw_x_limits, sw_y_limits, sw_window_size, sw_overlap):
windows = slide_window(dst,x_start_stop=x_limits,y_start_stop=y_limits,xy_window=window_size,xy_overlap=overlap)
hot_windows = search_windows(image, windows, svc, X_scaler, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
all_hot_windows.extend (hot_windows)
dst = draw_boxes(dst, hot_windows, color=(0, 0, 1), thick=4)
return all_hot_windows, dst
def get_heat_map(image, bbox_list):
heatmap = np.zeros_like(image[:,:,0]).astype(np.float)
# Iterate through list of bboxes
for box in bbox_list:
# Add += 1 for all pixels inside each bbox
heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1
# Return updated heatmap
return heatmap
class AverageHotBox ():
def __init__ (self, box):
self.avg_box = [list(p) for p in box]
self.detected_count = 1
self.boxes = [box]
def get_strength (self):
return self.detected_count
def get_box (self):
if len(self.boxes) > 1:
center = np.average (np.average (self.boxes, axis=1), axis=0).astype(np.int32).tolist()
# getting all x and y coordinates of
# all corners of joined boxes separately
xs = np.array(self.boxes) [:,:,0]
ys = np.array(self.boxes) [:,:,1]
half_width = int(np.std (xs))
half_height = int(np.std (ys))
return (
(
center[0] - half_width,
center[1] - half_height
), (
center[0] + half_width,
center[1] + half_height
))
else:
return self.boxes [0]
def is_close (self, box):
x11 = self.avg_box [0][0]
y11 = self.avg_box [0][1]
x12 = self.avg_box [1][0]
y12 = self.avg_box [1][1]
x21 = box [0][0]
y21 = box [0][1]
x22 = box [1][0]
y22 = box [1][1]
x_overlap = max(0, min(x12,x22) - max(x11,x21))
y_overlap = max(0, min(y12,y22) - max(y11,y21))
area1 = (x12 - x11) * (y12 - y11)
area2 = (x22 - x21) * (y22 - y21)
intersection = x_overlap * y_overlap;
if (
intersection >= 0.2 * area1 or
intersection >= 0.2 * area2
):
return True
else:
return False
def join (self, boxes):
joined = False
for b in boxes:
if self.is_close (b):
boxes.remove (b)
self.boxes.append (b)
self.detected_count += 1
self.avg_box [0][0] = min (self.avg_box [0][0], b [0][0])
self.avg_box [0][1] = min (self.avg_box [0][1], b [0][1])
self.avg_box [1][0] = max (self.avg_box [1][0], b [1][0])
self.avg_box [1][1] = max (self.avg_box [1][1], b [1][1])
joined = True
return joined
def calc_average_boxes (hot_boxes, strength):
avg_boxes = []
while len(hot_boxes) > 0:
b = hot_boxes.pop (0)
hb = AverageHotBox (b)
while hb.join (hot_boxes):
pass
avg_boxes.append (hb)
boxes = []
for ab in avg_boxes:
if ab.get_strength () >= strength:
boxes.append (ab.get_box ())
return boxes
In [17]:
# algorithm demonstration on test images
test_images = []
test_images_titles = []
for impath in glob.glob('test_images/test*.jpg'):
image = cv2.imread(impath)
# hot boxes
hot_boxes, image_with_hot_boxes = get_hot_boxes (image)
# heat map
heat_map = get_heat_map (image, hot_boxes)
# average boxes
avg_boxes = calc_average_boxes (hot_boxes, 2)
image_with_boxes = draw_boxes(image, avg_boxes, color=(0, 0, 1), thick=4)
test_images.append (image_with_hot_boxes)
test_images.append (heat_map)
test_images.append (image_with_boxes)
test_images_titles.extend (['', '', ''])
test_images_titles [0] = 'hot boxes'
test_images_titles [1] = 'heat map'
test_images_titles [2] = 'average boxes'
show_images_in_table (test_images, (3, 6), fig_size=(20, 24), titles=test_images_titles)
In [23]:
class HotBoxesHistory ():
"""Class for accumulation of hot boxes from last 10 frames
"""
def __init__ (self):
self.queue_max_len = 10 # number items to store
self.last_boxes = []
def put_hot_boxes (self, boxes):
"""Put frame hot boxes
"""
if (len(self.last_boxes) > self.queue_max_len):
tmp = self.last_boxes.pop (0)
self.last_boxes.append (boxes)
def get_hot_boxes (self):
"""Get last 10 frames hot boxes
"""
return_boxes = []
for boxes in self.last_boxes:
return_boxes.extend (boxes)
return return_boxes
old_hot_boxes = HotBoxesHistory ()
In [24]:
def process_image (image_orig):
image_orig = np.copy (image_orig)
image = cv2.cvtColor(image_orig, cv2.COLOR_RGB2BGR)
hot_boxes, image_with_hot_boxes = get_hot_boxes (image)
old_hot_boxes.put_hot_boxes (hot_boxes)
hot_boxes = old_hot_boxes.get_hot_boxes ()
avg_boxes = calc_average_boxes (hot_boxes, 10)
image_with_boxes = draw_boxes(image_orig, avg_boxes, color=(0, 0, 1), thick=4)
return image_with_boxes
image = (cv2.imread('test_images/test1.jpg'))
image = process_image(cv2.cvtColor(image,cv2.COLOR_BGR2RGB))
show_img(image)
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from moviepy.editor import VideoFileClip
def process_show(image):
image_out = process_image(image)
return image_out
output_v = 'final_video_resubmit3.mp4'
clip2 = VideoFileClip("project_video.mp4")
clip = clip2.fl_image(process_show)
%time clip.write_videofile(output_v, audio=False)
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